Discover how machines learn, adapt, and make intelligent predictions.
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Learn the core principles of supervised, unsupervised, and reinforcement learning.
Apply machine learning algorithms to real-world datasets.
Gain hands-on experience with popular libraries like scikit-learn and TensorFlow.

Course Overview
Course Overview: Machine Learning
Machine Learning explores algorithms and techniques that enable computers to learn patterns from data and make decisions without being explicitly programmed. Students will study supervised, unsupervised, and reinforcement learning, along with model evaluation, overfitting, and optimisation methods. The course blends theory with practical applications, giving learners experience with libraries such as scikit-learn or TensorFlow. By the end, students will understand how to design, train, and evaluate models for tasks such as classification, regression, clustering, and recommendation systems.
Learn the core principles of supervised, unsupervised, and reinforcement learning.
Apply machine learning algorithms to real-world datasets.
Gain hands-on experience with popular libraries like scikit-learn and TensorFlow.
Understand key concepts like overfitting, bias-variance trade-off, and model evaluation.
Prepare for careers in AI, data science, and advanced computing fields.
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Learn the core principles of supervised, unsupervised, and reinforcement learning.

Apply machine learning algorithms to real-world datasets.

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Free course. No credit card required.
Learn Machine Learning